See www.BIguidebook.com for companion material such as templates, examples, vendor links, and updated research. There, you will be able to subscribe to an email list and receive notices of updates, additions, and other occasional news that relates to the book. I will also use my blog at www.datadoghouse.com to post updates.

If you are a professor choosing this book as a text book, contact us at [email protected] for a syllabus.

Note: You may notice that this book does not use the word “user” any more than absolutely necessary. I explain my reasons in Chapter 17: People, Process and Politics, but, briefly, it is because we are building BI solutions for people. This is an important mind-set, seeing as BI projects are about people as much as they are about technology.

For this book and other related titles, see the publisher’s website: http://booksite.elsevier.com/9780124114616.

Chapter 1: The Business Demand for Data, Information, and Analytics sets the stage, and is important background for all audiences. It explains how the deluge of data and its accompanying need for analysis makes BI critical for the success of today’s enterprise. There is a big difference between raw data and actionable information. While there are attempts to circumvent BI with operational systems, there really is no good substitute for true BI.

Chapter 2: Justifying BI helps the BI team make both the business and technical case to determine the need, identify the benefits, and, most importantly, set expectations. Identifying risks and an organization’s readiness is critical to determining realistic expectations. This chapter covers determining the scope, plan, budget, and return on investment.

Chapter 3: Defining Requirements—Business, Data, and Quality discusses the process of creating the foundation of a successful BI solution by documenting what you are planning to build. The development team then uses these requirements to design, develop, and deploy BI systems. This is one of the most people-oriented processes in a project, making it especially tricky. Use this chapter to understand the roles and workflow, and how to conduct the interviews that are the basis for the requirements you will be documenting.

Chapter 4: Architecture Introduction helps everyone understand the importance of a well-architected foundation. The architecture sets your directions and goals. It is a set of guiding principles, but is flexible enough to allow for incremental growth. One of the key concepts discussed in this chapter is that of the accidental architecture, which is what happens when there is no plan.

Chapter 5: Information Architecture introduces the framework that defines the business context—“what, who, where, and why”—necessary for building successful BI solutions. An information architecture helps tame the deluge of data with a combination of processes, standards, people, and tools that establish information as a corporate asset. Using a data integration framework as a blueprint, you can transform data into consistent, quality, timely information for your business people to use in measuring, monitoring, and managing the enterprise. This gives your enterprise a better overall view of its customers and helps consolidate critical data.

Chapter 6: Data Architecture explains that data architecture is a blueprint that helps align your company’s data with its business strategies. This is where the book gets more technical, as it delves into the history of data architecture, the different choices available, and details on the analytical data architecture. It covers types of workflows, then explains operational data stores. It introduces the hybrid dimensional-normalized model.

Chapter 7: Technology and Product Architecture gets into the nitty gritty of the technology and product architectures and what you should know when you are evaluating them. It does not name products and vendors, as these can change frequently as companies merge and are acquired. If you want names, see the book Web site www.BIguidebook.com.

Chapter 8: Foundational Data Modeling, Chapter 9: Dimensional Modeling, and Chapter 10: Advanced Dimensional Modeling are aimed at technical members of the BI team who will be involved with creating data models, which are the cornerstone to building BI applications.

Chapter 8: Foundational Data Modeling describes the different levels of models: conceptual, logical and physical; the workflow, and where they are used. It explains entity relational (ER) modeling in depth,and covers normalization, the formal data modeling approach for validating a model.

Chapter 9: Dimensional Modeling compares ER versus dimensional modeling and provides details on the latter that is better suited to BI. Dimensional modeling uses facts, dimensions, and attributes, which can be organized in different ways, called schemas. The chapter covers dimensional modeling concepts such as date, time, role-playing, and degenerative dimensions, as well as event and consolidated fact tables.

Chapter 10: BI Dimensional Modeling gives you a strong understanding of how to develop a dimensional model and how dimensional modeling fits in your enterprise. It covers hierarchies, slowly changing dimensions, rapidly changing dimensions, causal dimensions, multivalue dimensions, and junk dimensions. The chapter also takes a closer look at snowflakes, as well as concepts such as value-band reporting, heterogeneous products, hot swappable dimensions.

Chapter 11: Data Integration Design and Development introduces data integration (DI), where the bulk of the work in a BI project lies. The best approaches are holistic, incremental, and iterative. The DI architecture represents the workflow of source data as it is transformed to become actionable information. The chapter covers the DI design steps for creating each stage’s process model and determining the design specifications. Standards are important for successful DI and it covers how to develop and apply them. Because historical data is often needed, it explains what to watch out for when loading it. It wraps up with discussions on prototyping and testing.

Chapter 12: Data Integration Processes takes the DI discussion to the next level, covering why it is best to use a DI tool (as opposed to hand-coding) and how to choose the best fit. These tools cover many services, including access and delivery, data profiling, data transformation, data quality, process management, operations management, and data transport; the data ingestion services change data capture, slowly changing dimensions, and reference look-ups.

Chapter 13: BI Applications talks about specifying the content of the BI application you are building, and the idea of using personas to make sure it resonates with your intended audience. It guides you on designing the layout as well as the function and form of the data. Matching the type of visualizations you create to the analysis that will be done makes the application much more effective.

Chapter 14: BI Design and Development covers the next step, which is to design the BI application’s visual layout and how it interacts with its users. This includes creating and adhering to standards for the user interface and standards for how information is accessed from the perspectives of privacy and security. You will learn the different methods for working on the design of the components, prototyping, developing the application, and then testing.

Chapter 15: Advanced Analytics shows how you can use analytics not just to learn about what has happened, but also to gauge the future and act on predictions. Predictive analytics includes the processes by which you use analytics for forecasting and modeling. Analytical sandboxes and hubs are two self-service data environments that help business people manage their own analytical needs, although IT is needed to create the data backbone. The chapter addresses the challenges of Big Data analytics, including scoping, architecting, and staffing a program.

Chapter 16: Data Shadow Systems sheds light on these frequently-seen departmental systems (usually spreadsheets) that business groups create and use to gather and analyze data on their own when they do not want to work with IT or cannot wait for them. Data shadow systems create silos, resulting in inconsistent data across the enterprise. The BI team needs to identify them and either replace them or incorporate them into the overall BI program.

Chapter 17: People, Process, and Politics delves into the stickiest of BI project issues. Technology is easy; it is people that are hard. This chapter lays out the relationship between the business and IT groups, discussing who does what, how they interact, and how to build the project management and development teams. It covers training for both business and IT people and provides a firm foundation in data governance—a people-centric program that is critical for transforming data into actionable information.

Chapter 18: Project Management stresses the need for an enterprise-wide BI program, which helps the BI project manager better plan and manage. The assessment is another arrow in the project manager’s quiver, helping to create a project plan that meets its requirements. The chapter gets into details on the work breakdown structure and project methodology choices. It guides you through all phases of the project and its schedule.

Chapter 19: Centers of Excellence discusses these organizational teams that address the problem of disconnected application and data silos across an enterprise. The business intelligence center of excellence (BI COE) coordinates and oversees BI activities including resources and expertise. The data integration COE (DI COE) team focuses specifically on data integration, establishing its scope, defining its architecture and vision, and helping to implement that vision.